alterlab-shap

What problem does it solve?

SHAP provides principled explanations for ML model predictions by attributing output changes to individual features using Shapley values, enabling both local and global interpretability.

Core Features & Use Cases

• Supports multiple explainers (TreeExplainer, DeepExplainer, LinearExplainer, KernelExplainer) for tree-based, deep learning, linear, and black-box models.
• Generates SHAP values, interaction values, and a variety of visualizations (beeswarm, waterfall, bar, scatter, heatmap, force) to diagnose feature importance, interactions, and fairness.
• Facilitates debugging, model validation, feature engineering, model comparison, and production deployment of explanations across data analytics tasks.

Quick Start

Select an appropriate SHAP explainer for your model, compute SHAP values on your data, and visualize the results to interpret feature contributions.